Calibration method for distributed sensor system

ABSTRACT

Methods of calibrating sensors in a distributed sensor system including a set of spatially distributed base units in communication with a central server over a data network include using a reference sensor, using a reference base unit, using crowd-sourced calibration, using sensor data collected in the same base unit, using sensor cross-sensitivity, or using sensor data from known environmental conditions.

BACKGROUND OF THE INVENTION

Air quality is a measure of the condition of air relative to therequirements of human need or purpose. Outdoor air quality monitoring isperformed to measure the levels of pollutants in the air so as to detectpotential harmful air pollution. Outdoor air quality monitoring istypically carried out using monitoring station installations in variousphysical locations. These monitoring stations measure the presence ofcontaminants in the air, such as carbon monoxide, ozone, particulatematter, sulphur dioxide (SO₂) and carbon dioxide (CO₂). Indoor airquality monitoring is becoming a matter of interest as the air inenclosed spaces, such as home, schools or workplaces, can also bepolluted. Conventional air quality monitors are expensive and requirecomplex calibration procedure to ensure accurate functions.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the invention are disclosed in the followingdetailed description and the accompanying drawings.

FIG. 1 is a system diagram illustrating an embodiment of a distributedsensor system for measuring air quality in an environment.

FIG. 2 illustrates an exposed perspective view of a base unit inembodiments of the present invention.

FIG. 3 illustrates a functional block diagram of a base unit inembodiments of the present invention.

FIG. 4 illustrates the organization of a calibration data table inembodiments of the present invention.

FIG. 5 is a flow chart illustrating a centralized backend calibrationmethod in embodiments of the present invention.

FIG. 6 is a flow chart illustrating an in-situ calibration method inembodiments of the present invention.

FIG. 7 is a flow chart illustrating a daisy-chained in-situ calibrationmethod in embodiments of the present invention.

FIG. 8 illustrates the organization of a calibration data table inembodiments of the present invention.

DETAILED DESCRIPTION

The invention can be implemented in numerous ways, including as aprocess; an apparatus; a system; a composition of matter; a computerprogram product embodied on a computer readable storage medium; and/or aprocessor, such as a processor configured to execute instructions storedon and/or provided by a memory coupled to the processor. In thisspecification, these implementations, or any other form that theinvention may take, may be referred to as techniques. In general, theorder of the steps of disclosed processes may be altered within thescope of the invention. Unless stated otherwise, a component such as aprocessor or a memory described as being configured to perform a taskmay be implemented as a general component that is temporarily configuredto perform the task at a given time or a specific component that ismanufactured to perform the task. As used herein, the term ‘processor’refers to one or more devices, circuits, and/or processing coresconfigured to process data, such as computer program instructions.

A detailed description of one or more embodiments of the invention isprovided below along with accompanying figures that illustrate theprinciples of the invention. The invention is described in connectionwith such embodiments, but the invention is not limited to anyembodiment. The scope of the invention is limited only by the claims andthe invention encompasses numerous alternatives, modifications andequivalents. Numerous specific details are set forth in the followingdescription in order to provide a thorough understanding of theinvention. These details are provided for the purpose of example and theinvention may be practiced according to the claims without some or allof these specific details. For the purpose of clarity, technicalmaterial that is known in the technical fields related to the inventionhas not been described in detail so that the invention is notunnecessarily obscured.

FIG. 1 is a system diagram illustrating an embodiment of a distributedsensor system for measuring air quality in an environment. Referring toFIG. 1, a distributed sensor system 100 including a set of spatiallydistributed base units 120, also referred to as “sensor nodes” or“nodes,” configured to obtain measurements from the environment. In FIG.1, sensor nodes A to C (120A to 120C) are shown. The sensor nodes insystem 100 may be deployed in an indoor location or an outdoor locationor both for monitoring the local air quality. In some embodiments, a setof sensor nodes are strategically deployed in a location to obtainsufficient amount of measurements to assess the air quality in alocation. For example, in one embodiment, a set of sensor nodes arespatially dispersed in a building, such as a workplace or a factory, tomonitor the air quality in the building.

Each base unit 120 includes one or more sensor modules (S#) and acontroller (CTR) which incorporates therein a transceiver. A sensormodule S# incorporates a sensor for detecting a specific air qualityparameter. The sensor modules may include different types of sensors forsensing different air quality parameters. A set of base units 120deployed in an installation may be configured with sensor modules withthe same sensor type. For example, nodes A, B and C all include sensormodules S1 and S2. A set of base units 120 may also be configured withsensor modules with different sensor types. For example, nodes B and Cinclude the sensor module S3 and not Node A. A salient feature of thebase unit 120 in the distributed sensor system of the present inventionis that the base unit is configurable to allow any desired types ofsensors to be incorporated for measuring the desired environmentparameters, as will be described in more detail below.

Each of the base units 120 includes a transceiver to communicate with acentral server 102 through a data network 110. The base units 120 mayemploy wired communication, such as Ethernet, or wirelesscommunications, such as radio frequency (RF), WiFi, ZigBee or other lowpower short-range wireless communication protocols. The controller CTRcontrols the sensing and communication functions of the base unit.

Central server 102 includes a data processor 104, a calibration datadatabase 106, and a sensor data database 108. Central server 102 storesin the sensor data database 108 raw sensor data received from the baseunits 120 over the data network 110. Data processor 104 is configured toprocess the raw sensor data using calibration data stored in thecalibration database 106 to generate calibrated sensor data which canthen be used to assess the air quality at the installation where sensornodes 120 are deployed. The calibrated sensor data may also be stored inthe sensor data database 108. The calibrated sensor data, as well as theraw sensor data, may be accessible through one or more applicationprogramming interface (API) to allow users to monitor the air qualitymeasurements obtained by the distributed sensor system 100.

Another salient feature of the distributed sensor system of the presentinvention is that each sensor module provides raw sensor data to thecentral server to be stored and processed. In the present description,raw sensor data refers to sensor data that has not been modified orcalibrated based on performance characteristics of the particular sensorthat generated the sensor data. Most sensors have certain amount ofnon-linearity characteristics over time and sensors need to becalibrated for the non-linearity or performance drift over time.Typically, a sensor may have gain or offset that drifts over time as thesensor is being used in an environment. Conventional sensors are oftencalibrated periodically, such as annually, and the calibration data,such as a gain correction value and an offset correction value, for thesensor is stored with the sensor itself and sensor data is modifiedusing the calibration data as the sensor data is being generated by thesensor. In the conventional sensors, when a sensor's characteristicsdrift over time before the next calibration update, the calibration datamay no longer be accurate for that sensor. However, the sensor willcontinue to use the inaccurate calibration data to calibrate or modifythe sensor data. Thus, conventional sensors may end up generating sensordata that has embedded calibration error and the sensor data ispermanently corrupted. In embodiments of the present invention, thedistributed sensor system 100 implements a centralized backendcalibration method where the base unit 120 reports raw sensor data thathave not been modified to the central server 102. Calibration of the rawsensor data is performed at the central server 102 to generatecalibrated sensor data using calibration data stored at the centralserver. The availability of the raw sensor data allows the centralserver to generate calibrated sensor data using updated or correctedcalibration data so that accuracy of the sensor measurement can beassured. More importantly, when calibration data for a sensor is foundto be inaccurate later on and new calibration data is generated, thecentral sever may regenerate the calibrated sensor data by retrievingthe raw sensor data for that sensor and calibrating the raw sensor dataagain using updated calibration data. In this manner, historic sensordata can be corrected if the calibration data used was found to beinaccurate. Correction of historic sensor data is not possible inconventional sensors because the calibration was done at the sensor andraw sensor data is typically not available. The centralized backendcalibration method in the distributed sensor system of the presentinvention will be described in more detail below.

In FIG. 1, the calibration data and the sensor data are shown as beingstored in two databases. The two databases can be two physical databasesor two logical databases. The exact configuration of the two databasesis not critical to the practice of the present invention. The storage ofthe calibration data and the sensor data can be made in one physicaldata storage or multiple physical data storage.

FIG. 2 illustrates an exposed perspective view of a base unit inembodiments of the present invention. Referring to FIG. 2, a base unit120, or sensor node 120, is enclosed in a housing 130 having air vents136 formed there on to enable air diffusion or air flow into the housingto reach the sensors housed therein. In most embodiments, base unit 120is enclosed entirely by housing 130. In the present illustration, thetop of housing 130 is omitted to illustrate the internal construction ofthe base unit. Base unit 120 includes a printed circuit board 132 onwhich a controller 180 is formed. The controller 180 may include one ormore integrated circuits formed on the printed circuit board 132. Forexample, controller 180 may include a processor integrated circuit and amemory integrated circuit for supporting the operation of the base unit.Controller 180 may also include a transceiver circuit to enable the baseunit to communicate with a data network. Controller 180 may beconfigured to provide wired communication or wireless communication witha data network. To support wired communication, base unit 120 mayinclude data ports, such as USB port 137 and Ethernet port 138, toconnect to a data network through wired connections. The USB port 137may also be used to daisy chain two or more base units together, such asfor calibration purposes, as will be explained in more detail below. Tosupport wireless communication, base unit 120 may include an antenna(not shown).

In embodiments of the present invention, base unit 120 incorporatestherein one or more sensor modules 140. Each sensor module 140 includesa sensor of a particular sensor type for sensing a given air qualityparameter. Each sensor module 140 provides digital measurement data asan output signal. Each sensor module 140 is equipped with a universalconnector to be coupled to a matching receptacle 134 formed on theprinted circuit board 132. The printed circuit board 132 includes a setof matching receptacles 134 to receive one or more sensor modules 140.In the present embodiment, the universal connector is an USB connector.As thus constructed, base unit 120 can be configured to include sensormodules of the desired sensor types. The configurable base unit 120 canbe configured with sensor modules of the same sensor type or sensormodules of different sensor types. Furthermore, sensor modules 140 withuniversal connectors are removably attached to the base unit so that abase unit can be readily reconfigured by removing and replacing thesensor modules. For example, a sensor module may be replaced with a newsensor module when malfunctioning is detected; or a sensor module of onesensor type may be replaced with a sensor module of a different sensortype to monitor a different air quality parameter of interest.

Each of the matching receptacles 134 is coupled to a data bus formed onthe printed circuit board 132 which connects to the controller 180. Asensor module 140 coupled to a matching receptacle communicates with thecontroller 180 through the data bus. Regardless of the number and typesof sensor modules incorporated in a base unit, controller 180 controlsand coordinates the transfer of measurement data from each of the sensormodule to the data network and onto the central server.

FIG. 3 illustrates a functional block diagram of a base unit inembodiments of the present invention. Referring to FIG. 3, a base unit120 (or “a sensor node”) includes a controller 180 in communication withone or more sensor modules 140 and configured to control the operationof the base unit. In the present illustration, the base unit includesthree sensor modules 140-1 to 140-3. Each sensor module 140 providesdigital measurement data to controller 180 through a data bus 152. Inone embodiment, data bus 152 is a serial data bus.

Controller 180 includes a processor 184, a memory 186 and a set oftransceivers 182. Controller 180 communicates with each sensor moduleand coordinates the receipt and transfer of digital measurement datafrom the sensor modules to the data network. In the present embodiment,controller 180 includes a set of transceivers 182 to provide both wiredand wireless communication. In this manner, when base unit 120 isdeployed at an installation, the base unit may communicate with a datanetwork using any one of a variety of communication protocols that maybe available at that installation. In the present embodiment, controller180 includes transceivers supporting Ethernet, WiFi, Bluetooth andwireless mesh networking. In other embodiments, controller 180 maysupport other wired or wireless communication protocols, such as ZigBeeor other low power short-range wireless communication protocols.

Sensor module 140 includes a sensor 142 of a specific sensor type formeasuring a given air quality parameter. For example, sensor 142 may bea sensor to measure contaminants in the air, such as a carbon monoxide(CO) sensor, an ozone (O₃) sensor, a sulphur dioxide (SO₂) sensor, or acarbon dioxide (CO₂) sensor. Sensor 142 may also be a sensor formeasuring particulate matter in the air. Sensor 142 typically provides acurrent value or a voltage value as an analog output signal. The analogoutput signal is coupled to an analog front-end circuit 144 whichamplifies and conditions the analog output signal. The amplified analogoutput signal is then provided to an analog-to-digital converter (ADC)146 to be digitized into a digital sensor output signal. The digitalsensor output signal is provided to a microcontroller 148 whichcommunicates with an interface 150 to transmit digital sensor outputsignal onto the data bus 152. In the present embodiment, microcontroller148 formats the digital sensor output signal as a serial data stream tobe transmitted on the data bus 152. The configuration of sensor module140 in FIG. 2 is illustrative only and is not intended to be limiting.The exact configuration of sensor module 140 is not critical to thepractice of the present invention as long as the sensor module providesdigital sensor output signal on a data bus to the controller 180. Inother embodiments, the sensor 142 may provide a digital sensor outputsignal directly. In that case, sensor module 140 does not need toinclude an analog front-end circuit or an ADC.

As thus configured, each of sensor modules 140-1 to 140-3 measures anair quality parameter specified by the sensor 142. Each sensor module140-1 to 140-3 provides digital sensor output signal to controller 180.Controller 180 in the base unit 120 receives the digital sensor outputsignals from the one or more sensor modules 140-1 to 140-3. Controller180 may store the digital sensor output signals in memory 186 pendingtransfer to the central server. Controller 180 connects to the datanetwork through one of the communication protocols supported bytransceivers 182. Controller 180 operates to transfer the digital sensoroutput signals as the raw sensor data to the central server for furtherprocessing. It is instructive to note that controller 180 does notmodify or calibrate the digital sensor output signals and no calibrationdata associated with the sensors 142 is stored in the base unit.

Referring to FIGS. 1-3, in distributed sensor system 100, a number ofsensor nodes 120 may be deployed in an installation. Each of the sensornodes provides raw sensor data of the measurements it has taken to thecentral server. Distributed sensor system 100 implements a centralizedbackend calibration scheme to perform calibration of the raw sensor dataat the central server to relieve each sensor node from the burden ofstoring calibration data and calibrating the measurement data. Thecentralized backend calibration scheme also has the benefit of enhancingsensor data integrity as the raw sensor data can be calibrated usingcorrected or updated calibration data in the future.

In embodiments of the present invention, the central server 102 storescalibration data for all of the sensors deployed in the installation inthe database 106. The calibration data for the sensors may originatefrom the manufacturer. A sensor may be provided with a set ofcalibration data, e.g. gain and offset correction values, during finaltesting of the sensor at the manufacturing site. A sensor may also betested before deployment or at deployment to obtain a set of calibrationdata for that sensor. In embodiments of the present invention, a set ofcalibration data for a sensor is identified by a sensor identifier(sensor ID) and a board identifier (board ID) so that each set ofcalibration data is uniquely associated with a specific sensor connectedto a specific printed circuit board of a sensor node. Furthermore, theraw sensor data provided by each sensor in a sensor node is similarlyidentified so that each set of raw sensor data is uniquely associatedwith a specific sensor on a specific printed circuit board of a sensornode. For instance, the controller 180 appends the board ID and thesensor ID to the digital sensor output signal for each sensor modulebefore the digital sensor output signal is transmitted to the centralserver. In this manner, when the raw sensor data is transmitted to thecentral server, the raw sensor data can be readily paired with theassociated calibration data through the board identifier and the sensoridentifier.

As shown in FIG. 2, a sensor node 120 includes a printed circuit board132 having a controller 180 formed thereon. The board ID is used touniquely identify a printed circuit board 132 and the controller 180formed thereon in a base unit. In other words, the board ID uniquelyidentifies a base unit or a sensor node. In one embodiment, the board IDis the MAC address assigned to the printed circuit board. The sensor IDuniquely identifies a sensor module 140 or a sensor in the sensor module140. In one embodiment, the sensor ID is the serial number of the sensorin a sensor module 140. Thus, by using the board ID and the sensor ID,the calibration data and raw sensor data for a sensor incorporated in asensor node 120 can be uniquely identified. The combination of the boardID and the sensor ID identifies a specific sensor incorporated in aspecific sensor node having the specific board ID. More importantly, asensor module 140 may be removed from one sensor node and incorporatedinto another sensor node. The raw sensor data for that sensor modulewill now be associated with a new board ID even though the sensor IDremains the same. By identifying the sensor data (raw sensor data orcalibration data) using both the board ID and the sensor ID,traceability of the sensor data for a sensor is maintained regardless ofwhich sensor node the sensor is incorporated in.

FIG. 4 illustrates the organization of a calibration data table inembodiments of the present invention. The calibration data table may bestored in the calibration data base 106 in the central server 102.Referring to FIG. 4, a calibration data table 200 includes multipleentries of calibration data where each entry is identified by a sensorID and a board ID. For each entry of a given sensor ID and a given boardID, a set of calibration data values for that sensor is stored. In someembodiments, each entry is also associated with a valid date range and acalibration timestamp indicating the day range or the time range whenthe calibration data is considered valid. For example, the distributedsensor system 100 may perform a calibration of certain sensors once aweek. In that case, the calibration data for that sensor will be given avalid date range (start date and end date) and the date the calibrationis taken is recorded in the time stamp. If, for some reason, a week ispassed and re-calibration was not performed, the central server wouldknow from the valid date range in table 200 that the calibration datafor that sensor has expired.

In embodiments of the present invention, the calibration data values fora sensor include one or more calibration terms, including, but notlimited to, a gain correction value, an offset correction value, a slopecorrection value, and a non-linear scaling value. The calibration termsmay further include a leakage current correction value, a temperaturecompensation factor, and ADC settings. In some applications, thecalibration data table may be set up to include a given set ofcalibration terms, such as gain and offset correction and a newcalibration term is later found to improve the accuracy of the sensormeasurements. For example, a correction term for the ADC gain may beintroduced to improve the accuracy of the sensor measurements. In thatcase, the calibration data table 200 may be updated to include the newcalibration term. The central server can regenerate the calibratedsensor data using the raw sensor data stored in the sensor data database108 and the updated calibration data stored in the calibration datadatabase 106 for each affected sensor. In this manner, the centralizedbackend calibration scheme enables accurate historic sensor dataanalysis.

Calibration data table 200 in FIG. 4 is illustrative only. In otherembodiments, the calibration data table may store additional dataassociated with each sensor ID. For example, the calibration data tablemay include data identifying the sensor type or other referenceinformation. The calibration data table may further include a validindicator to indicate whether the calibration data for that sensor IDand that board ID should be used for calibrating raw sensor data. Analternate embodiment of a calibration data table is shown in FIG. 8.

FIG. 5 is a flow chart illustrating a centralized backend calibrationmethod in embodiments of the present invention. Referring to FIG. 5, acentralized backend calibration method 250 may be implemented in thecentral server 102 in the distributed sensor system 100 for generatingcalibrated sensor data from raw sensor data received from one or moresensor nodes in the installation. At 252, method 250 maintainscalibration data for each sensor in each sensor node in system 100. Inone embodiment, the calibration data is stored in the calibration datadatabase 106 and the calibration data is maintained in a calibrationdata table. Each entry of the calibration data is identified by thesensor ID and the board ID. At 254, central server 102 receives rawsensor data, in the form of digital sensor output signals, from one ormore sensor nodes over the data network. At 256, the data processor 104in the central server 102 identifies the raw sensor data by the sensorID and the board ID associated with each piece of raw sensor data.Method 250 may store the raw sensor data in the sensor data database108.

At 258, based on the identified sensor ID and the board ID, the dataprocessor 104 retrieves the calibration data values associated with thesensor ID and the board ID. At 260, the data processor 104 then adjustsor modifies or calibrates the raw sensor data using the calibration datavalues retrieved for that sensor ID and that board ID. In this manner,method 250 generates calibrated sensor data (262). The calibrated sensordata may be stored in the sensor data database 108.

Method 250 in FIG. 5 describes the backend calibration method forgenerating calibrated sensor data for raw sensor data received from oneor more sensor nodes. Method 250 may also be applied to performhistorical sensor data analysis. That is, for raw sensor data that isstored in the sensor data database, method 250 can be applied torecalculate the calibrated sensor data, such as when the calibrationdata values for one or more sensors have been updated. In that case,instead of receiving the raw sensor data from the sensor nodes, method250 retrieves the raw sensor data for a sensor ID and a board ID storedin the sensor data database. The retrieved raw sensor data can then bepaired with calibration data values stored in the calibration datadatabase through the sensor ID and the board ID. The data processor 104generates updated calibrated sensor data from the retrieved raw sensordata and updated calibration data values.

In the distributed sensor system of the present invention, the accuracyof the sensor measurements is largely dependent on the accuracy of thecalibration data for each sensor. As sensor performance has a tendencyto drift over time, calibration data obtained at one point in time maybecome inaccurate over time and introduces error to the calibratedsensor data. Therefore, it is often necessary to perform recalibrationof sensors periodically to update the calibration data values for thesensors. In embodiments of the present invention, the distributed sensorsystem implements one or more sensor calibration methods to facilitaterecalibration of the sensors in the base units in an installation toensure the accuracy of the calibration data in the central server.

In one embodiment, the distributed sensor system implements an in-situgolden sensor calibration method using a “golden” sensor or a referencesensor. In the present description, a “golden sensor” or a “referencesensor” refers to a sensor with known and verified performancecharacteristics to which another sensor of the same type can be testedor compared against. Referring again to FIG. 2, a base unit 120 includesa data port 137 which may be used to facilitate the in-situ goldensensor calibration. In embodiments of the present invention, a sensormodule containing a golden sensor of the same sensor type as one of thesensor modules in the base unit is connected to the data port 137. Insome cases, the golden sensor should be placed near the module undertest for some time period to allow the golden sensor to acclimate to thesame environmental condition as the module under test.

With a golden sensor thus coupled, the golden sensor takes sensormeasurement data which is transmitted to the central server through theoperation of controller 180 with the sensor ID of the golden sensor andthe board ID of the base unit 120. The central server identifies the rawsensor data received as the golden or reference sensor measurementoriginated from a golden sensor by the sensor ID and also identifies thebase unit to which the golden sensor is attached by the board ID.Meanwhile, the central server also receives raw sensor data as testsensor measurement from the sensor under test in the base unitidentified by the sensor ID of the sensor under test and the board ID.Because the golden sensor is placed in the vicinity of the sensor undertest, the sensor measurements generated by the golden sensor and thesensor under test should be about the same. The central server,receiving the golden sensor measurement and the test sensor measurementcan thus derive the correction factors to be applied to the sensor undertest to improve the accuracy of the sensor measurements. The updatedcalibration data values are stored in the calibration data table for thesensor under test, as identified by the sensor ID and the board ID.

When the in-situ golden sensor calibration method is used, each sensormodule in a base unit is calibrated in turn by supplying a golden sensormodule and coupling the golden sensor module to the data port 137. Whenthe base unit includes multiple sensor modules, the one-by-onecalibration method may become impractical. In another embodiment of thepresent invention, the distributed sensor system implements an in-situgolden base unit calibration method using a “golden” base unit or areference base unit. In the present description, a “golden base unit” ora “reference base unit” refers to a base unit including a given set ofsensor modules with known and verified performance characteristics towhich another base unit with the same sensor modules or a subset of thesensor modules can be tested or compared against. When a golden baseunit is used, the golden base unit is placed in the vicinity of a baseunit under test. In some cases, the golden base unit should be placednear the base unit under test for some time period to allow the goldenbase unit to acclimate to the same environmental condition as the baseunit under test.

With the golden base unit thus situated, the golden base unit takessensor measurement data which is transmitted to the central serverthrough the operation of controller 180 with the sensor ID of each ofthe sensor modules thereon and the board ID of the golden base unit. Thecentral server identifies the raw sensor data received as golden orreference sensor measurements originated from a golden base unit by thesensor ID of the sensor modules and the board ID of the golden baseunit. Meanwhile, the central server also receives raw sensor data astest sensor measurements from the sensor modules of the base unit undertest identified by the sensor ID and the board ID of the base unit undertest. Because the golden base unit is placed in the vicinity of the baseunit under test, the sensor measurements generated by the golden baseunit and the base unit under test should be about the same. The centralserver, receiving the golden sensor measurements of the golden base unitand the test sensor measurements of the base unit under test can thusderive the correction factors to be applied to the sensors in the baseunit under test to improve the accuracy of the sensor measurements. Theupdated calibration data values are stored in the calibration data tablefor the sensor modules in the base unit under test, as identified by thesensor ID and the board ID.

The in-situ golden base unit calibration method is applied to calibrateall of the sensor modules in a base unit collectively. In someembodiments, when multiple base units are deployed in close vicinity toeach other, the same golden base unit can be used to calibrate a groupof the base units that are installed within a given distance from thegolden base unit. In some embodiments, each base unit is equipped with aGPS positioning device and the central server uses the GPS positioningdata of each base unit to determine the group of base units that is inclose proximity to the golden base unit.

FIG. 6 is a flow chart illustrating an in-situ calibration method inembodiments of the present invention. The in-situ calibration method canbe implemented using a golden sensor or a golden base unit as describedabove. Referring to FIG. 6, an in-situ calibration method 270 starts at272 when the central server receives sensor measurements that areidentified as being from a golden sensor, which can be a golden sensormodule connected to a base unit under test or a golden sensor in agolden base unit. At 274, the central server identifies the sensormeasurements as golden sensor measurements by the sensor ID and theboard ID. In the case of a golden sensor calibration, the sensor IDidentifies the golden sensor while the board ID identifies the base unitto which the golden sensor is attached. In the case of a golden baseunit calibration, the sensor ID and the board ID identifies the goldenbase unit.

At 276, the central server receives test sensor measurements from thesensor under test. In the case of a golden sensor calibration, thesensor ID identifies the sensor under test while the board ID identifiesthe base unit to which the golden sensor is attached. In the case of agolden base unit calibration, the sensor ID and the board ID identifiesa sensor module in the base unit under test. The base unit under testcan be one or more base units that are located in close physicalproximity to the golden base unit. At 278, method 270 assesses thecalibration data values from the golden sensor measurements and the testsensor measurement from the sensor under test. At 280, method 270 storesthe updated calibration data values for the sensor under test in thecalibration data table.

In one embodiment, a multi-point calibration is performed. That is,multiple golden sensor measurements and multiple test sensormeasurements for the sensor under test are taken under differentenvironmental conditions, for example, during the day and during thenight, during work hours or during off-work hours. Calibration datavalues obtained from multiple sensor measurements usually give betterresults.

In another embodiment, a single-point calibration is performed at highfrequency. That is, the calibration data are computed from golden sensormeasurement taken under one environmental condition but the method 270is repeated periodically and at high frequency. By updating thecalibration data frequently, the accuracy of the calibration data isalso improved.

In embodiments of the present invention, a golden base unit and a baseunit under test are connected in a daisy chain or a master-slaveconfiguration. In this manner, the sensor data from the base unit undertest is automatically associated with the golden sensor data so that thecentral server can readily identify the sensor being calibrated and thegolden sensor data to use for the calibration. When a golden base unitis connected in a master-slave configuration with a base unit undertest, golden sensor measurements are transmitted through the base unitunder test to the central server. The controller in the base unit undertest appends its board ID (“test board ID”) to sensor ID/board ID of thegolden base unit so that the central server can identify the goldensensor measurements as being provided through the base unit under test.In this manner, any subsequent sensor measurement received with the sametest board ID identifies those sensor measurements as being from thebase unit under test.

FIG. 7 is a flow chart illustrating a daisy-chained in-situ calibrationmethod in embodiments of the present invention. Referring to FIG. 7, adaisy-chained in-situ calibration method 300 starts at 302 when thecentral server receives sensor measurements that are identified as beingfrom a golden base unit transmitted through the base unit under test. At304, the central server identifies the sensor measurements as goldensensor measurements by the sensor ID and the board ID of the golden baseunit. The central server further identifies the base unit under test bythe board ID appended to the golden sensor measurements. The board ID ofthe base unit under test will be referred to as the “test board ID.”

At 306, the central server receives sensor measurements from the baseunit under test as the test sensor measurements. At 308, method 300assesses the calibration data values from the golden sensor measurementsand the test sensor measurement from the base unit under test. At 310,method 300 stores the updated calibration data values for the sensormodules in the base unit under test in the calibration data table. Whenthe golden sensor measurements are funneled through the base unit undertest to be transmitted to the central server, the golden sensormeasurements and the sensor measurements for the base unit under testare automatically associated with each other so that calibration datafor the base unit under test can be readily updated.

According to other aspects of the present invention, the distributedsensor system may perform calibration of the sensors in the base unitswithout the use of any reference sensor or reference base unit. In oneembodiment, the distributed sensor system performs crowd-sourcedcalibration based on the network effect of a group of neighboringsensors of the same sensor type. In the distributed sensor system, thereis usually an array of base units deployed in an installation when agroup of the base units are in close enough vicinity of each other tomeasure similar environmental conditions. The performance of a group ofneighboring base units with expected similar sensor measurements can beused to calibrate the sensors in that group of base units. In oneembodiment, neighboring base units with expected similar sensormeasurements are designated as belonging to a calibration group. Thecentral server compares the sensor measurements of a given sensor in thebase units of the calibration group. Based on the analysis of the sensormeasurements from the calibration group, the central server maydetermine that the calibration data values for sensors in some of thebase units may need to be compensated. The central server may thenupdate the calibration data values for the sensors in the base units andstore the updated calibration data in the calibration data database.

According to another aspect of the present invention, the distributedsensor system performs calibration of the sensors in a base unit usingsensor data associated with ambient conditions collected in the samebase unit. In one embodiment, a base unit includes a temperature sensorand/or a humidity sensor. Most sensors for measuring air qualityparameters have a temperature dependency and/or humidity dependency. Inembodiments of the present invention, the central server collectstemperature and/or humidity data recorded by a base unit and use thetemperature and/or humidity data to calibrate the sensor measurementsgenerated by the air quality sensors in the same base unit. In thismanner, the temperature and/or humidity dependency can be removed fromthe sensor measurements of the base unit and the sensor measurements canbe made more accurate.

In yet another aspect of the present invention, the distributed sensorsystem performs calibration of the sensors in a base unit based onsensor cross-sensitivity. Some air quality sensors havecross-sensitivity with the potential to respond to contaminants otherthan the target contaminant. Thus, two sensor types designed for twodifferent target contaminants may respond to some degree to the samecontaminant in the air. For example, ozone sensor and nitric oxidesensor have a given degree of sensor cross-sensitivity. In that case, ina base unit equipped with an ozone sensor and a nitric oxide sensor, thesensor measurements from both sensors should correlate with each otherto some degree as a result of the cross-sensitivity. That is, if thesensor measurement from the ozone sensor indicated increased detectedlevel, the nitric oxide sensor should also indicate a similar increasein detected level. In embodiments of the present invention, the centralserver monitors and calibrates sensor performances by analyzing sensormeasurements from sensors with known cross-sensitivity. In oneembodiment, the central server receives sensor measurements from a firstgroup of sensors of a first sensor type and sensor measurements from asecond group of sensors of a second sensor type having a sensorcross-sensitivity with the first sensor type. The central servergenerates compensation to the calibration data based on the sensormeasurements of the first and second group of sensors.

Furthermore, in embodiments of the present invention, the central serverreceives sensor measurements from a first group of sensors of a firstsensor type and sensor measurements from a second group of sensors of asecond sensor type having a sensor cross-sensitivity with the firstsensor type. The central server determines specification of thedifferent gases, that is, the concentration of individual gases, basedon the sensor measurements and the sensors cross-sensitivitycharacteristics. In one embodiment, by analyzing the sensor measurementsbased on the known cross-sensitivity, the concentration of a given gascan be determined.

In yet another aspect of the present invention, the distributed sensorsystem performs calibration of the sensors based on known environmentalconditions. In particular, the central server performs calibration of asensor by analyzing sensor measurements from the sensor that were takenunder the same environment conditions over a period of time. Forexample, a base unit is installed in an office and the office isunoccupied between the hours of midnight and dawn. The central servermay collect sensor measurements from the sensor at the same time (e.g. 2am) over a period of time (e.g. 6 months). The central server analyzessensor measurements taken from the sensor under the same environmentalcondition over a period of time to determine a trend. The central serverdetermines compensation to the calibration data of the sensor based onthe trend analysis.

In some embodiments, a base unit stores sensor calibration data for thesensor modules incorporated therein in the base unit's local memory.Referring to FIG. 3, the base unit 120 may store calibration data forthe sensor modules 140-1 to 140-3 in the memory 186 on controller 180.The controller still operates to transmit raw sensor data to the centralserver. However, in embodiments of the present invention, the controller180 performs local calibration of the raw sensor data and generateslocally calibrated sensor data. The locally calibrated sensor data maybe stored in memory 186.

Although the foregoing embodiments have been described in some detailfor purposes of clarity of understanding, the invention is not limitedto the details provided. There are many alternative ways of implementingthe invention. The disclosed embodiments are illustrative and notrestrictive.

What is claimed is:
 1. A method of calibrating sensors in a distributedsensor system comprising a plurality of spatially distributed base unitsin communication with a central server over a data network, each baseunit comprising one or more sensors configured to measure an air qualityparameter, the method comprising: receiving a reference sensormeasurement from a reference sensor in a first base unit; identifyingthe reference sensor measurement by a sensor identifier uniquelyidentifying the reference sensor in the first base unit and a boardidentifier uniquely identifying the first base unit; receiving a testsensor measurement from a first sensor under test; assessing calibrationdata values for the first sensor under test using the reference sensormeasurement and the test sensor measurement; and storing updatedcalibration data values for the first sensor under test.
 2. The methodof claim 1, wherein the first sensor under test is provided in the firstbase unit and the reference sensor is coupled to a data port of thefirst base unit, and the method further comprises: identifying the testsensor measurement by a sensor identifier uniquely identifying the firstsensor under test and a board identifier uniquely identifying the firstbase unit.
 3. The method of claim 1, wherein the first base unitcomprises a reference base unit and the first sensor under test isformed in a second base unit, the reference base unit being placed inthe vicinity of the second base unit, and the method further comprises:identifying the test sensor measurement by a sensor identifier uniquelyidentifying the first sensor under test and a board identifier uniquelyidentifying the second base unit.
 4. The method of claim 1, wherein thefirst base unit comprises a reference base unit and the first sensorunder test is formed in a second base unit, and the method furthercomprises: connecting the reference base unit in a master-slaveconfiguration with the second base unit; and transmitting the referencesensor measurement through the second base unit.
 5. The method of claim4, further comprising: identifying the reference sensor measurement by asensor identifier uniquely identifying the reference sensor in the firstbase unit, a board identifier uniquely identifying the reference baseunit, and a test board identifier uniquely identifying the second baseunit; identifying the test sensor measurement by a sensor identifieruniquely identifying the first sensor under test and a board identifieruniquely identifying the second base unit; and associating the referencesensor measurement and the sensor measurement from the first sensorunder test using the board identifier of the second base unit.
 6. Themethod of claim 1, further comprising: receiving reference sensormeasurements from the reference sensor taken at a plurality ofenvironmental conditions; receiving test sensor measurements from thefirst sensor under test taken at the plurality of environmentalconditions; and assessing calibration data values for the first sensorunder test using the reference sensor measurements and the test sensormeasurements taken at the plurality of environmental conditions.
 7. Themethod of claim 3, further comprising: receiving test sensormeasurements from a plurality of sensors under test in a plurality ofbase units, the plurality of base units being positioned within a givendistance from the reference base unit; assessing calibration data valuesfor the plurality of sensors under test using the reference sensormeasurement and the test sensor measurements for each of the sensorsunder test; and storing updated calibration data values for theplurality of sensors under test.
 8. The method of claim 7, furthercomprising: determining a base unit with a sensor under test to bewithin a given distance from the reference base unit using positioningdata from the base unit and the reference base unit.
 9. A method ofcalibrating sensors in a distributed sensor system comprising aplurality of is spatially distributed base units in communication with acentral server over a data network, each base unit comprising one ormore sensors configured to measure an air quality parameter, the methodcomprising: receiving sensor measurements from sensors of the samesensor type in neighboring base units belonging to a calibration group;analyzing sensor measurements for the sensors in the calibration group;assessing calibration data values for one or more sensors in thecalibration group using the sensor measurements received from thecalibration group; and storing updated calibration data values for theone or more sensors.
 10. The method of claim 9, further comprising:associating neighborhood base units in the calibration group whensensors of the same sensor type in the base units are expected togenerate similar sensor measurements.
 11. A method of calibratingsensors in a distributed sensor system comprising a plurality ofspatially distributed base units in communication with a central serverover a data network, each base unit comprising one or more sensorsconfigured to measure an air quality parameter, the method comprising:receiving a sensor measurement associated with air quality from a firstsensor in a first base unit; receiving a sensor measurement associatedwith an ambient condition from the first base unit; assessingcalibration data values for the first sensor using the sensormeasurement associated with the ambient condition; and storing updatedcalibration data values for the first sensor.
 12. The method of claim11, wherein receiving a sensor measurement associated with an ambientcondition from the first base unit comprises: receiving a sensormeasurement associated with temperature from the first base unit. 13.The method of claim 11, wherein receiving a sensor measurementassociated with an ambient condition from the first base unit comprises:receiving a sensor measurement associated with humidity from the firstbase unit.
 14. A method of calibrating sensors in a distributed sensorsystem comprising a plurality of spatially distributed base units incommunication with a central server over a data network, each base unitcomprising one or more sensors configured to measure an air qualityparameter, the method comprising: associating a first sensor in a baseunit with a second sensor in the base unit having sensorcross-sensitivity; receiving sensor measurements from the first sensorand the second sensor; analyzing sensor measurements from the firstsensor and the second sensor; assessing calibration data values for thefirst and second sensors based on the sensor measurements from the firstand second sensors; and storing updated calibration data values for thefirst and second sensors.
 15. The method of claim 14, furthercomprising: is determining a concentration of a gas measured by thefirst sensor based on the sensor measurements from the first and secondsensors and the sensor cross-sensitivity characteristics.
 16. A methodof calibrating sensors in a distributed sensor system comprising aplurality of spatially distributed base units in communication with acentral server over a data network, each base unit comprising one ormore sensors configured to measure an air quality parameter, the methodcomprising: receiving a plurality of sensor measurements from a firstsensor in a first base unit under a known environment condition over aperiod of time; analyzing the plurality of sensor measurements todetermine a trend; assessing calibration data values for the firstsensor based on the trend analysis; and storing updated calibration datavalues for the first sensor.